Abstract:

It is essential that teachers analyze learning styles as each student can have a different style and will need a personalized approach to optimize their academic performance. By recognizing and adapting to students’ learning styles, teachers can create a more effective and propitious learning environment for each person.
Continuous improvement or improvement in teaching practice is essential to ensure that educators are prepared to face the changing challenges in the educational environment. The training and constant updating allow teachers to acquire new pedagogical tools, strategies and methodologies that benefit the learning of their students, combined with statistical tools that help to make decisions.

There are several statistical tools that can be used to analyze learning styles. Below, I will mention some of the most common:
Learning Style Questionnaires: Questionnaires specifically designed to assess students' learning styles can be administered. These questionnaires may include questions covering different dimensions of learning, such as preference for visual, auditory or kinesthetic learning, preference for individual or group learning, among others. Statistical techniques can then be used to analyze the results and classify students into different categories of learning styles.
Factor analysis: Factor analysis is a technique used to identify underlying patterns in a set of variables. It can be applied to the results of the learning styles questionnaires to group and categorize the different styles in more general dimensions. For example, it might reveal that certain students tend to have a more visual and kinesthetic learning style, while others have a more auditory and reflective style.

Cluster analysis:
This technique allows students to be grouped into clusters or groups based on their responses to the learning styles questionnaires. Students within the same cluster will have similar response patterns, which will make it possible to identify homogeneous groups with similar learning styles.
Regression and correlation analysis: These techniques are used to determine if there is any relationship between learning styles and academic performance. It can be analyzed if certain learning styles are associated with better performance or if certain styles are related to academic difficulties.
Analysis of variance (ANOVA): it can be applied to compare the means of different groups of students with different learning styles in terms of their academic performance accompanied by the Tukey test.

Nonparametric Tests:
If the data does not meet the assumptions of ANOVA or other parametric tests, nonparametric tests, such as the Kruskal-Wallis test, can be used to compare learning style groups in terms of academic performance.
It is important to select the appropriate statistical tools based on the nature of the data and the specific objectives of the analysis. The validity and reliability of the learning styles questionnaires used should also be taken into account to ensure accurate and meaningful results for them using Cronbach's alpha.

Keywords:

Learning styles, Factorial analysis, Cluster analysis, Regression and correlation analysis, ANOVA, Nonparametric Tests.